Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package includes implementations of several recent advances in recurrence based analysis, among them applications to multivariate data and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.

Moreno I. Coco , Dan Mønster , Giuseppe Leonardi , Rick Dale , Sebastian Wallot
2020-04-30

CRAN packages used

tseriesChaos, nonlinearTseries, RHRV, crqa

CRAN Task Views implied by cited packages

TimeSeries, Finance

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Citation

For attribution, please cite this work as

Coco, et al., "The R Journal: Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa", {The R Journal}, 2020

BibTeX citation

@article{RJ-2021-062,
  author = {Coco, Moreno I. and Mønster, Dan and Leonardi, Giuseppe and Dale, Rick and Wallot, Sebastian},
  title = {The R Journal: Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa},
  journal = {{The R Journal}},
  year = {2020},
  note = {https://doi.org/10.32614/RJ-2021-062},
  doi = {10.32614/RJ-2021-062},
  volume = {13},
  issue = {1},
  issn = {2073-4859},
  pages = {145-163}
}